The role of the Data Analyst is to take the technical data of Power BI and arrange it into a story that helps in decision making.A messy data foundation forces leadership to rely on instinct rather than insight, this can lead to the business collapsing.
Steps in cleaning a messy data
Power Query
It is a data transformation and data preparation engine. It allows you to connect to hundreds of different data sources eg Excel and clean that data.
What power query does:
- Removing null values, splitting columns (e.g., "City, State" into two columns), changing data types, and filtering out unnecessary rows.
Every change you make is recorded in the Applied Steps pane on the right. Power Query doesn't "save over" your data; it just remembers the steps to clean it.
Once the data is polished and structured, you click "Close & Apply." Power Query then sends the clean data into the Power BI "Data Model," where it is ready for DAX measures and visualization.
DAX & The Data Model
DAX stands for Data Analysis Expressions
Its job is to analyze the data already in the room.
Its looks at the Physical Tables Power Query created and calculates new values ( using New Measures) based on how you interact with the report.
It also creates Virtual Values These don't exist as physical columns in your data; they are calculated on the fly.
Every DAX measure follows a specific structure.
_Measure Name = FUNCTION{'Table'[Column])_
-
The Name: Always wrap it in brackets
[Total Sales]when referencing it elsewhere. The Function: Common ones include
SUM, AVERAGE, COUNTROWS,or the ofDAX: CALCULATE.The Reference: DAX likes specific addresses—'
Sales'[Revenue]tells it exactly which table and column to look at.
Conclusion
Power BI is the bridge between "the messy data" and "the optimized business". The true skill of an analyst isn't just knowing how to write a CALCULATE function; it’s knowing which business question that function is answering.
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